Overview

Dataset statistics

Number of variables13
Number of observations161
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.6 KiB
Average record size in memory112.0 B

Variable types

Numeric13

Alerts

Alcohol is highly overall correlated with Color_Intensity and 1 other fieldsHigh correlation
Malic_Acid is highly overall correlated with HueHigh correlation
Ash_Alcanity is highly overall correlated with FlavanoidsHigh correlation
Magnesium is highly overall correlated with ProlineHigh correlation
Total_Phenols is highly overall correlated with Flavanoids and 2 other fieldsHigh correlation
Flavanoids is highly overall correlated with Ash_Alcanity and 5 other fieldsHigh correlation
Nonflavanoid_Phenols is highly overall correlated with Flavanoids and 1 other fieldsHigh correlation
Proanthocyanins is highly overall correlated with Flavanoids and 2 other fieldsHigh correlation
Color_Intensity is highly overall correlated with AlcoholHigh correlation
Hue is highly overall correlated with Flavanoids and 1 other fieldsHigh correlation
OD280 is highly overall correlated with Flavanoids and 3 other fieldsHigh correlation
Proline is highly overall correlated with Alcohol and 1 other fieldsHigh correlation

Reproduction

Analysis started2024-02-03 01:02:30.229375
Analysis finished2024-02-03 01:02:51.000777
Duration20.77 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Alcohol
Real number (ℝ)

HIGH CORRELATION 

Distinct115
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.040373
Minimum11.41
Maximum14.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-02-02T20:02:51.123607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum11.41
5-th percentile11.81
Q112.37
median13.07
Q313.71
95-th percentile14.22
Maximum14.83
Range3.42
Interquartile range (IQR)1.34

Descriptive statistics

Standard deviation0.79784075
Coefficient of variation (CV)0.061182358
Kurtosis-0.95514277
Mean13.040373
Median Absolute Deviation (MAD)0.67
Skewness-0.031846875
Sum2099.5
Variance0.63654986
MonotonicityNot monotonic
2024-02-02T20:02:51.295616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.08 5
 
3.1%
12.37 5
 
3.1%
12.29 4
 
2.5%
13.05 4
 
2.5%
12.42 3
 
1.9%
12.25 3
 
1.9%
12 3
 
1.9%
13.49 2
 
1.2%
13.11 2
 
1.2%
12.85 2
 
1.2%
Other values (105) 128
79.5%
ValueCountFrequency (%)
11.41 1
0.6%
11.45 1
0.6%
11.61 1
0.6%
11.62 1
0.6%
11.64 1
0.6%
11.65 1
0.6%
11.66 1
0.6%
11.76 1
0.6%
11.81 1
0.6%
11.82 2
1.2%
ValueCountFrequency (%)
14.83 1
0.6%
14.75 1
0.6%
14.39 1
0.6%
14.38 2
1.2%
14.37 1
0.6%
14.3 1
0.6%
14.23 1
0.6%
14.22 2
1.2%
14.21 1
0.6%
14.2 1
0.6%

Malic_Acid
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3068323
Minimum0.74
Maximum5.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-02-02T20:02:51.465511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile1.09
Q11.61
median1.86
Q33.03
95-th percentile4.31
Maximum5.19
Range4.45
Interquartile range (IQR)1.42

Descriptive statistics

Standard deviation1.0482631
Coefficient of variation (CV)0.45441669
Kurtosis-0.22163062
Mean2.3068323
Median Absolute Deviation (MAD)0.51
Skewness0.89398769
Sum371.4
Variance1.0988555
MonotonicityNot monotonic
2024-02-02T20:02:51.628291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.73 7
 
4.3%
1.81 4
 
2.5%
1.35 3
 
1.9%
1.53 3
 
1.9%
1.61 3
 
1.9%
1.9 3
 
1.9%
1.68 2
 
1.2%
1.5 2
 
1.2%
1.66 2
 
1.2%
1.83 2
 
1.2%
Other values (112) 130
80.7%
ValueCountFrequency (%)
0.74 1
0.6%
0.89 1
0.6%
0.9 1
0.6%
0.92 1
0.6%
0.94 1
0.6%
0.98 1
0.6%
1.01 1
0.6%
1.07 1
0.6%
1.09 1
0.6%
1.1 1
0.6%
ValueCountFrequency (%)
5.19 1
0.6%
5.04 1
0.6%
4.95 1
0.6%
4.72 1
0.6%
4.61 1
0.6%
4.6 1
0.6%
4.43 1
0.6%
4.36 1
0.6%
4.31 1
0.6%
4.3 1
0.6%

Ash
Real number (ℝ)

Distinct71
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3632919
Minimum1.7
Maximum2.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-02-02T20:02:51.787137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.7
5-th percentile1.94
Q12.23
median2.36
Q32.53
95-th percentile2.73
Maximum2.92
Range1.22
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.24120473
Coefficient of variation (CV)0.10206303
Kurtosis0.047438664
Mean2.3632919
Median Absolute Deviation (MAD)0.15
Skewness-0.21566106
Sum380.49
Variance0.05817972
MonotonicityNot monotonic
2024-02-02T20:02:51.955332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.28 7
 
4.3%
2.3 7
 
4.3%
2.36 6
 
3.7%
2.32 6
 
3.7%
2.7 5
 
3.1%
2.38 5
 
3.1%
2.62 4
 
2.5%
2.48 4
 
2.5%
2.4 4
 
2.5%
2.1 4
 
2.5%
Other values (61) 109
67.7%
ValueCountFrequency (%)
1.7 2
1.2%
1.71 1
 
0.6%
1.88 1
 
0.6%
1.9 1
 
0.6%
1.92 3
1.9%
1.94 1
 
0.6%
1.98 3
1.9%
1.99 1
 
0.6%
2 2
1.2%
2.02 1
 
0.6%
ValueCountFrequency (%)
2.92 1
 
0.6%
2.87 1
 
0.6%
2.86 1
 
0.6%
2.84 1
 
0.6%
2.8 1
 
0.6%
2.75 1
 
0.6%
2.74 2
 
1.2%
2.73 1
 
0.6%
2.72 2
 
1.2%
2.7 5
3.1%

Ash_Alcanity
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.232298
Minimum11.2
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-02-02T20:02:52.126587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum11.2
5-th percentile15
Q117.1
median19
Q321
95-th percentile24.5
Maximum27
Range15.8
Interquartile range (IQR)3.9

Descriptive statistics

Standard deviation3.0147057
Coefficient of variation (CV)0.15675223
Kurtosis0.060302389
Mean19.232298
Median Absolute Deviation (MAD)2
Skewness0.032577247
Sum3096.4
Variance9.0884503
MonotonicityNot monotonic
2024-02-02T20:02:52.625406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 15
 
9.3%
21 11
 
6.8%
16 11
 
6.8%
18 10
 
6.2%
19 8
 
5.0%
18.5 7
 
4.3%
21.5 6
 
3.7%
19.5 6
 
3.7%
22.5 6
 
3.7%
22 6
 
3.7%
Other values (49) 75
46.6%
ValueCountFrequency (%)
11.2 1
0.6%
11.4 1
0.6%
12 1
0.6%
12.4 1
0.6%
13.2 1
0.6%
14 2
1.2%
14.6 1
0.6%
15 2
1.2%
15.2 2
1.2%
15.5 2
1.2%
ValueCountFrequency (%)
27 1
 
0.6%
26.5 1
 
0.6%
26 1
 
0.6%
25.5 1
 
0.6%
25 2
 
1.2%
24.5 3
1.9%
24 5
3.1%
23.6 1
 
0.6%
23.5 1
 
0.6%
23 2
 
1.2%

Magnesium
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.534161
Minimum70
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-02-02T20:02:53.537339image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile80
Q188
median98
Q3106
95-th percentile120
Maximum134
Range64
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.36529
Coefficient of variation (CV)0.12549241
Kurtosis-0.13112891
Mean98.534161
Median Absolute Deviation (MAD)9
Skewness0.49840832
Sum15864
Variance152.90039
MonotonicityNot monotonic
2024-02-02T20:02:53.750311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
88 12
 
7.5%
86 10
 
6.2%
101 9
 
5.6%
98 8
 
5.0%
96 7
 
4.3%
102 7
 
4.3%
94 6
 
3.7%
85 5
 
3.1%
112 5
 
3.1%
97 5
 
3.1%
Other values (37) 87
54.0%
ValueCountFrequency (%)
70 1
 
0.6%
78 3
 
1.9%
80 5
3.1%
81 1
 
0.6%
82 1
 
0.6%
84 3
 
1.9%
85 5
3.1%
86 10
6.2%
87 3
 
1.9%
88 12
7.5%
ValueCountFrequency (%)
134 1
 
0.6%
132 1
 
0.6%
128 1
 
0.6%
127 1
 
0.6%
126 1
 
0.6%
123 1
 
0.6%
122 1
 
0.6%
121 1
 
0.6%
120 3
1.9%
118 3
1.9%

Total_Phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2903727
Minimum0.98
Maximum3.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-02-02T20:02:53.919324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.98
5-th percentile1.38
Q11.72
median2.35
Q32.8
95-th percentile3.27
Maximum3.88
Range2.9
Interquartile range (IQR)1.08

Descriptive statistics

Standard deviation0.63236648
Coefficient of variation (CV)0.27609763
Kurtosis-0.82420853
Mean2.2903727
Median Absolute Deviation (MAD)0.51
Skewness0.082184211
Sum368.75
Variance0.39988736
MonotonicityNot monotonic
2024-02-02T20:02:54.087234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 8
 
5.0%
3 6
 
3.7%
2.6 5
 
3.1%
2 5
 
3.1%
2.95 5
 
3.1%
2.8 5
 
3.1%
1.38 4
 
2.5%
2.85 4
 
2.5%
2.45 4
 
2.5%
1.65 4
 
2.5%
Other values (80) 111
68.9%
ValueCountFrequency (%)
0.98 1
 
0.6%
1.1 1
 
0.6%
1.15 1
 
0.6%
1.25 1
 
0.6%
1.28 1
 
0.6%
1.3 1
 
0.6%
1.35 1
 
0.6%
1.38 4
2.5%
1.39 2
1.2%
1.4 2
1.2%
ValueCountFrequency (%)
3.88 1
0.6%
3.85 1
0.6%
3.52 1
0.6%
3.5 1
0.6%
3.4 1
0.6%
3.38 1
0.6%
3.3 2
1.2%
3.27 1
0.6%
3.25 2
1.2%
3.2 1
0.6%

Flavanoids
Real number (ℝ)

HIGH CORRELATION 

Distinct123
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0437888
Minimum0.34
Maximum3.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-02-02T20:02:54.256222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.52
Q11.22
median2.14
Q32.91
95-th percentile3.49
Maximum3.93
Range3.59
Interquartile range (IQR)1.69

Descriptive statistics

Standard deviation0.9856495
Coefficient of variation (CV)0.48226582
Kurtosis-1.2190429
Mean2.0437888
Median Absolute Deviation (MAD)0.84
Skewness-0.13176957
Sum329.05
Variance0.97150493
MonotonicityNot monotonic
2024-02-02T20:02:54.432115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.58 3
 
1.9%
2.65 3
 
1.9%
2.03 3
 
1.9%
1.25 3
 
1.9%
2.69 2
 
1.2%
0.5 2
 
1.2%
2.53 2
 
1.2%
2.68 2
 
1.2%
0.47 2
 
1.2%
0.66 2
 
1.2%
Other values (113) 137
85.1%
ValueCountFrequency (%)
0.34 1
 
0.6%
0.47 2
1.2%
0.48 1
 
0.6%
0.49 1
 
0.6%
0.5 2
1.2%
0.51 1
 
0.6%
0.52 1
 
0.6%
0.55 1
 
0.6%
0.56 1
 
0.6%
0.58 3
1.9%
ValueCountFrequency (%)
3.93 1
0.6%
3.75 1
0.6%
3.74 1
0.6%
3.69 1
0.6%
3.67 1
0.6%
3.64 1
0.6%
3.56 1
0.6%
3.54 1
0.6%
3.49 1
0.6%
3.4 1
0.6%

Nonflavanoid_Phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)23.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35813665
Minimum0.13
Maximum0.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-02-02T20:02:54.589466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.19
Q10.27
median0.34
Q30.43
95-th percentile0.6
Maximum0.66
Range0.53
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation0.12181033
Coefficient of variation (CV)0.34012249
Kurtosis-0.48575333
Mean0.35813665
Median Absolute Deviation (MAD)0.08
Skewness0.51863118
Sum57.66
Variance0.014837756
MonotonicityNot monotonic
2024-02-02T20:02:54.740464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.26 11
 
6.8%
0.43 10
 
6.2%
0.29 10
 
6.2%
0.37 8
 
5.0%
0.27 8
 
5.0%
0.34 8
 
5.0%
0.32 8
 
5.0%
0.4 8
 
5.0%
0.3 7
 
4.3%
0.22 6
 
3.7%
Other values (28) 77
47.8%
ValueCountFrequency (%)
0.13 1
 
0.6%
0.14 1
 
0.6%
0.17 5
3.1%
0.19 2
 
1.2%
0.2 2
 
1.2%
0.21 5
3.1%
0.22 6
3.7%
0.24 5
3.1%
0.25 2
 
1.2%
0.26 11
6.8%
ValueCountFrequency (%)
0.66 1
 
0.6%
0.63 3
1.9%
0.61 3
1.9%
0.6 3
1.9%
0.58 2
 
1.2%
0.56 1
 
0.6%
0.55 1
 
0.6%
0.53 6
3.7%
0.52 2
 
1.2%
0.5 5
3.1%

Proanthocyanins
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5480745
Minimum0.41
Maximum2.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-02-02T20:02:54.899463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.41
5-th percentile0.73
Q11.24
median1.53
Q31.87
95-th percentile2.38
Maximum2.96
Range2.55
Interquartile range (IQR)0.63

Descriptive statistics

Standard deviation0.52585943
Coefficient of variation (CV)0.33968612
Kurtosis0.065809929
Mean1.5480745
Median Absolute Deviation (MAD)0.34
Skewness0.32964475
Sum249.24
Variance0.27652814
MonotonicityNot monotonic
2024-02-02T20:02:55.072766image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.35 9
 
5.6%
1.46 6
 
3.7%
1.87 5
 
3.1%
1.25 5
 
3.1%
1.56 4
 
2.5%
2.08 4
 
2.5%
1.98 4
 
2.5%
1.66 4
 
2.5%
1.04 3
 
1.9%
1.62 3
 
1.9%
Other values (80) 114
70.8%
ValueCountFrequency (%)
0.41 1
0.6%
0.42 1
0.6%
0.55 1
0.6%
0.62 1
0.6%
0.64 2
1.2%
0.68 1
0.6%
0.73 2
1.2%
0.75 1
0.6%
0.8 2
1.2%
0.81 1
0.6%
ValueCountFrequency (%)
2.96 1
 
0.6%
2.91 2
1.2%
2.81 3
1.9%
2.49 1
 
0.6%
2.45 1
 
0.6%
2.38 3
1.9%
2.35 1
 
0.6%
2.34 1
 
0.6%
2.29 2
1.2%
2.28 1
 
0.6%

Color_Intensity
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0141615
Minimum1.28
Maximum10.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-02-02T20:02:55.236766image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.28
5-th percentile2.15
Q13.27
median4.8
Q36.2
95-th percentile9.2
Maximum10.52
Range9.24
Interquartile range (IQR)2.93

Descriptive statistics

Standard deviation2.1173791
Coefficient of variation (CV)0.4222798
Kurtosis-0.2634575
Mean5.0141615
Median Absolute Deviation (MAD)1.5
Skewness0.62347552
Sum807.28
Variance4.4832944
MonotonicityNot monotonic
2024-02-02T20:02:55.396766image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.6 4
 
2.5%
3.8 4
 
2.5%
2.8 3
 
1.9%
3.05 3
 
1.9%
5.1 3
 
1.9%
5.7 3
 
1.9%
5.4 3
 
1.9%
5.6 3
 
1.9%
4.5 3
 
1.9%
3.3 2
 
1.2%
Other values (112) 130
80.7%
ValueCountFrequency (%)
1.28 1
0.6%
1.74 1
0.6%
1.95 1
0.6%
2 1
0.6%
2.06 2
1.2%
2.08 1
0.6%
2.12 1
0.6%
2.15 1
0.6%
2.2 1
0.6%
2.3 1
0.6%
ValueCountFrequency (%)
10.52 1
0.6%
10.26 1
0.6%
10.2 1
0.6%
9.899999 1
0.6%
9.7 1
0.6%
9.58 1
0.6%
9.4 1
0.6%
9.3 1
0.6%
9.2 1
0.6%
9.01 1
0.6%

Hue
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95910559
Minimum0.54
Maximum1.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-02-02T20:02:55.555295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.54
5-th percentile0.58
Q10.79
median0.98
Q31.12
95-th percentile1.27
Maximum1.45
Range0.91
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.21804941
Coefficient of variation (CV)0.22734662
Kurtosis-0.74962057
Mean0.95910559
Median Absolute Deviation (MAD)0.15
Skewness-0.14095231
Sum154.416
Variance0.047545545
MonotonicityNot monotonic
2024-02-02T20:02:55.716295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.04 8
 
5.0%
1.23 7
 
4.3%
1.12 6
 
3.7%
0.89 5
 
3.1%
0.96 5
 
3.1%
1.25 5
 
3.1%
1.07 4
 
2.5%
0.7 4
 
2.5%
1.19 4
 
2.5%
1.09 4
 
2.5%
Other values (63) 109
67.7%
ValueCountFrequency (%)
0.54 1
 
0.6%
0.55 1
 
0.6%
0.56 2
1.2%
0.57 3
1.9%
0.58 2
1.2%
0.59 2
1.2%
0.6 3
1.9%
0.61 2
1.2%
0.62 1
 
0.6%
0.65 1
 
0.6%
ValueCountFrequency (%)
1.45 1
 
0.6%
1.42 1
 
0.6%
1.38 1
 
0.6%
1.36 2
 
1.2%
1.33 1
 
0.6%
1.31 1
 
0.6%
1.28 1
 
0.6%
1.27 1
 
0.6%
1.25 5
3.1%
1.24 1
 
0.6%

OD280
Real number (ℝ)

HIGH CORRELATION 

Distinct112
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6288199
Minimum1.27
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-02-02T20:02:55.870297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.27
5-th percentile1.42
Q12.01
median2.78
Q33.18
95-th percentile3.58
Maximum4
Range2.73
Interquartile range (IQR)1.17

Descriptive statistics

Standard deviation0.70905164
Coefficient of variation (CV)0.26972241
Kurtosis-1.0238111
Mean2.6288199
Median Absolute Deviation (MAD)0.52
Skewness-0.36041727
Sum423.24
Variance0.50275422
MonotonicityNot monotonic
2024-02-02T20:02:56.025295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.87 4
 
2.5%
2.78 4
 
2.5%
3 4
 
2.5%
1.82 3
 
1.9%
1.33 3
 
1.9%
2.77 3
 
1.9%
2.96 3
 
1.9%
1.75 3
 
1.9%
3.33 3
 
1.9%
3.17 3
 
1.9%
Other values (102) 128
79.5%
ValueCountFrequency (%)
1.27 1
 
0.6%
1.29 2
1.2%
1.3 1
 
0.6%
1.33 3
1.9%
1.36 1
 
0.6%
1.42 1
 
0.6%
1.48 1
 
0.6%
1.51 2
1.2%
1.55 1
 
0.6%
1.56 2
1.2%
ValueCountFrequency (%)
4 1
0.6%
3.92 1
0.6%
3.82 1
0.6%
3.71 1
0.6%
3.64 1
0.6%
3.63 1
0.6%
3.59 1
0.6%
3.58 2
1.2%
3.57 1
0.6%
3.56 1
0.6%

Proline
Real number (ℝ)

HIGH CORRELATION 

Distinct114
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean759.1118
Minimum278
Maximum1680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-02-02T20:02:56.179287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile352
Q1502
median675
Q31035
95-th percentile1310
Maximum1680
Range1402
Interquartile range (IQR)533

Descriptive statistics

Standard deviation323.86647
Coefficient of variation (CV)0.42663869
Kurtosis-0.42789765
Mean759.1118
Median Absolute Deviation (MAD)205
Skewness0.69779162
Sum122217
Variance104889.49
MonotonicityNot monotonic
2024-02-02T20:02:56.341248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
680 5
 
3.1%
630 4
 
2.5%
625 4
 
2.5%
520 4
 
2.5%
750 3
 
1.9%
1035 3
 
1.9%
450 3
 
1.9%
510 3
 
1.9%
495 3
 
1.9%
1285 3
 
1.9%
Other values (104) 126
78.3%
ValueCountFrequency (%)
278 1
0.6%
290 1
0.6%
312 1
0.6%
315 1
0.6%
325 1
0.6%
342 1
0.6%
345 2
1.2%
352 1
0.6%
355 1
0.6%
365 1
0.6%
ValueCountFrequency (%)
1680 1
0.6%
1547 1
0.6%
1515 1
0.6%
1510 1
0.6%
1480 1
0.6%
1450 1
0.6%
1375 1
0.6%
1320 1
0.6%
1310 1
0.6%
1295 1
0.6%

Interactions

2024-02-02T20:02:49.076806image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:30.628222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:32.374533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:33.697366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:35.128583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:36.560217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:39.413246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:40.986318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:42.464496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:43.847193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:45.182202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:46.472755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:47.755962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:49.185809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:30.878158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:32.485620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:33.822482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:35.252866image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:36.673217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:39.641352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:41.105321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:42.574492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:43.960191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:45.291852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:46.580499image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:47.864313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:49.283805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:30.999156image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:32.577583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:33.951575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:35.355714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:36.772219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:39.758394image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:41.234283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:42.667493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:44.057193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:45.378627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:46.672141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:47.956899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:49.385067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:31.112152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:32.678428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:34.056978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:35.464714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:36.873218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:39.858416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:41.341275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:42.767492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:44.155184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:45.479820image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:46.766911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:48.057395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:49.496067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:31.229190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:32.785348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:34.169828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:35.578735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:38.438209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:39.978663image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:41.457283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:42.873503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:44.266328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:45.585492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:46.874365image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:48.167088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:49.604070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:31.354549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:32.891519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:34.277725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:35.690748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:38.549285image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:40.089382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:41.566282image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:42.988493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:44.372335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:45.685645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:46.977359image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:48.272485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:49.709069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:31.587541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:32.999228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:34.384279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:35.808736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:38.659698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:40.202582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:41.712972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:43.102504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:44.474043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:45.787584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:47.081500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:48.373327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:49.824067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:31.724545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:33.106549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:34.496596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:35.925473image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:38.769569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:40.323847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:41.824966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:43.212572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:44.582257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:45.894150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:47.185575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:48.483765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:49.991077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:31.830538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:33.202897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:34.600599image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:36.030476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:38.872088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:40.437312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:41.927848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:43.331564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:44.676691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:45.989390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:47.281330image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:48.581816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:50.166070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:31.943533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:33.302888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:34.702145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:36.141481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:38.977533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:40.558312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:42.039849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:43.440568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:44.780609image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:46.089232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:47.377217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:48.680808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:50.273068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:32.053534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:33.398273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:34.801016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:36.243473image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:39.084566image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:40.665317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:42.149490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:43.546568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:44.877937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:46.182292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:47.471095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:48.776819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:50.371080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:32.156536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:33.493747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:34.899041image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:36.344472image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:39.198277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:40.768317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:42.251503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:43.643573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:44.976220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:46.274616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:47.560479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:48.872819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:50.475219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:32.263547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:33.593478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:35.004156image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:36.448712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:39.302374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:40.873311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:42.354492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:43.745188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:45.077766image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:46.372431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:47.654423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T20:02:48.970805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-02T20:02:56.463429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
AlcoholMalic_AcidAshAsh_AlcanityMagnesiumTotal_PhenolsFlavanoidsNonflavanoid_PhenolsProanthocyaninsColor_IntensityHueOD280Proline
Alcohol1.0000.1330.237-0.3590.4460.3620.358-0.1970.2660.6300.0310.1490.648
Malic_Acid0.1331.0000.2110.2800.117-0.306-0.3630.258-0.2450.285-0.551-0.266-0.042
Ash0.2370.2111.0000.2780.4080.1340.0810.0690.0510.221-0.004-0.0010.291
Ash_Alcanity-0.3590.2800.2781.000-0.222-0.478-0.5260.372-0.344-0.142-0.366-0.376-0.478
Magnesium0.4460.1170.408-0.2221.0000.2620.239-0.2280.1300.4110.0040.0420.519
Total_Phenols0.362-0.3060.134-0.4780.2621.0000.896-0.4930.6840.0390.4750.6870.458
Flavanoids0.358-0.3630.081-0.5260.2390.8961.000-0.5820.784-0.0110.5550.7230.470
Nonflavanoid_Phenols-0.1970.2580.0690.372-0.228-0.493-0.5821.000-0.4440.020-0.263-0.521-0.275
Proanthocyanins0.266-0.2450.051-0.3440.1300.6840.784-0.4441.0000.0050.3720.5910.346
Color_Intensity0.6300.2850.221-0.1420.4110.039-0.0110.0200.0051.000-0.371-0.2990.495
Hue0.031-0.551-0.004-0.3660.0040.4750.555-0.2630.372-0.3711.0000.4640.205
OD2800.149-0.266-0.001-0.3760.0420.6870.723-0.5210.591-0.2990.4641.0000.266
Proline0.648-0.0420.291-0.4780.5190.4580.470-0.2750.3460.4950.2050.2661.000
2024-02-02T20:02:56.691217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
AlcoholAshAsh_AlcanityColor_IntensityFlavanoidsHueMagnesiumMalic_AcidNonflavanoid_PhenolsOD280ProanthocyaninsProlineTotal_Phenols
Alcohol1.0000.237-0.3590.6300.3580.0310.4460.133-0.1970.1490.2660.6480.362
Ash0.2371.0000.2780.2210.081-0.0040.4080.2110.069-0.0010.0510.2910.134
Ash_Alcanity-0.3590.2781.000-0.142-0.526-0.366-0.2220.2800.372-0.376-0.344-0.478-0.478
Color_Intensity0.6300.221-0.1421.000-0.011-0.3710.4110.2850.020-0.2990.0050.4950.039
Flavanoids0.3580.081-0.526-0.0111.0000.5550.239-0.363-0.5820.7230.7840.4700.896
Hue0.031-0.004-0.366-0.3710.5551.0000.004-0.551-0.2630.4640.3720.2050.475
Magnesium0.4460.408-0.2220.4110.2390.0041.0000.117-0.2280.0420.1300.5190.262
Malic_Acid0.1330.2110.2800.285-0.363-0.5510.1171.0000.258-0.266-0.245-0.042-0.306
Nonflavanoid_Phenols-0.1970.0690.3720.020-0.582-0.263-0.2280.2581.000-0.521-0.444-0.275-0.493
OD2800.149-0.001-0.376-0.2990.7230.4640.042-0.266-0.5211.0000.5910.2660.687
Proanthocyanins0.2660.051-0.3440.0050.7840.3720.130-0.245-0.4440.5911.0000.3460.684
Proline0.6480.291-0.4780.4950.4700.2050.519-0.042-0.2750.2660.3461.0000.458
Total_Phenols0.3620.134-0.4780.0390.8960.4750.262-0.306-0.4930.6870.6840.4581.000

Missing values

2024-02-02T20:02:50.633518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-02T20:02:50.867989image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AlcoholMalic_AcidAshAsh_AlcanityMagnesiumTotal_PhenolsFlavanoidsNonflavanoid_PhenolsProanthocyaninsColor_IntensityHueOD280Proline
014.231.712.4315.61272.803.060.282.295.641.043.921065
113.201.782.1411.21002.652.760.261.284.381.053.401050
213.162.362.6718.61012.803.240.302.815.681.033.171185
314.371.952.5016.81133.853.490.242.187.800.863.451480
413.242.592.8721.01182.802.690.391.824.321.042.93735
514.201.762.4515.21123.273.390.341.976.751.052.851450
614.391.872.4514.6962.502.520.301.985.251.023.581290
714.062.152.6117.61212.602.510.311.255.051.063.581295
814.831.642.1714.0972.802.980.291.985.201.082.851045
913.861.352.2716.0982.983.150.221.857.221.013.551045
AlcoholMalic_AcidAshAsh_AlcanityMagnesiumTotal_PhenolsFlavanoidsNonflavanoid_PhenolsProanthocyaninsColor_IntensityHueOD280Proline
16712.823.372.3019.5881.480.660.400.9710.2600000.721.75685
16813.582.582.6924.51051.550.840.391.548.6600000.741.80750
16913.404.602.8625.01121.980.960.271.118.5000000.671.92630
17012.203.032.3219.0961.250.490.400.735.5000000.661.83510
17112.772.392.2819.5861.390.510.480.649.8999990.571.63470
17214.162.512.4820.0911.680.700.441.249.7000000.621.71660
17413.403.912.4823.01021.800.750.431.417.3000000.701.56750
17513.274.282.2620.01201.590.690.431.3510.2000000.591.56835
17613.172.592.3720.01201.650.680.531.469.3000000.601.62840
17714.134.102.7424.5962.050.760.561.359.2000000.611.60560